Curation and Extraction of Drug-Related Entities from Reddit Platform
Not on the map. Why?
Abstract (arXiv)
Physicians learn primarily about illicit drugs from clinical overdose cases, limiting their understanding of real-world usage. Meanwhile, drug users share first-hand experiences online, offering insights into dosage and effects of drugs. To bridge this gap, we introduce ReDose (REddit Drug DOSe and Effect), a dataset of 6,435 Reddit posts on substance use. A board-certified toxicologist primarily annotated both the training and test sets, while two medical science students contributed to the test set, labeling DRUG, DOSE, and EFFECT entities. We benchmarked 6,267 annotations using BERT-based, large language model (LLM)-based, and Retrieval-Augmented Generation (RAG) models. BiomedBERT achieved an F1-score of 0.843 for DRUG, while Llama-3 70B outperformed GPT-4 (F1 = 0.79 vs. 0.72). EFFECT extraction remains challenging, with GPT-4 achieving a recall of 0.41. ReDose captures patient-curated narratives to advance medical data extraction from social media.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 8 Apr 2026.
Builds on
14 of 34 references.
References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2023GPT-4 Technical Reportcited by 18,132
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
- 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 996
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
- 2023GPT-NER: Named Entity Recognition via Large Language Modelscited by 158
- 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
- 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
- 2022Wojood: Nested Arabic Named Entity Corpus and Recognition using BERTcited by 32
- 2024Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Mediacited by 9
- 2024Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncologycited by 8
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models
- 2026Large language models can disambiguate opioid slang on social media
- 2025RAG-based Architectures for Drug Side Effect Retrieval in LLMs
- 2025HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
- 2024Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
- 2026PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text
- 2026Identifying Evidence-Based Nudges in Biomedical Literature with Large Language Models
- 2025GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance